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在无应答情况下使用校准权重的分层连续抽样中总体方差的改进估计。

Improved estimation of population variance in stratified successive sampling using calibrated weights under non-response.

作者信息

Pandey M K, Singh G N, Zaman Tolga, Mutairi Aned Al, Mustafa Manahil SidAhmed

机构信息

Department of Mathematics & Computing, Indian Institute of Technology (Indian School of Mines), Dhanbad, 826 004, Jharkhand, India.

Faculty of Health Sciences, Gumushane University, Gumushane, Turkey.

出版信息

Heliyon. 2024 Mar 12;10(6):e27738. doi: 10.1016/j.heliyon.2024.e27738. eCollection 2024 Mar 30.

DOI:10.1016/j.heliyon.2024.e27738
PMID:38545218
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10965521/
Abstract

This paper introduces a new method to estimate the population variance of a study variable in stratified successive sampling over two occasions, while accounting for random non-response. The method uses a logarithmic type estimator and leverages information from a highly positively correlated auxiliary variable. The paper also presents calibrated weights for the new estimator and examines its properties through numerical and simulation studies. The results indicate that the suggested estimator is more effective than the standard estimator for estimating the population variance.

摘要

本文介绍了一种新方法，用于在考虑随机无应答的情况下，估计两次分层连续抽样中研究变量的总体方差。该方法使用对数型估计量，并利用来自高度正相关辅助变量的信息。本文还给出了新估计量的校准权重，并通过数值研究和模拟研究检验了其性质。结果表明，所提出的估计量在估计总体方差方面比标准估计量更有效。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9a1/10965521/df37fd373333/gr007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9a1/10965521/bfd1a65477bf/gr001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9a1/10965521/20c2c7d0e768/gr002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9a1/10965521/5e067fa245b6/gr003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9a1/10965521/4398572c7329/gr004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9a1/10965521/6db4827d3493/gr005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9a1/10965521/dcc16ff0b686/gr006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9a1/10965521/df37fd373333/gr007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9a1/10965521/bfd1a65477bf/gr001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9a1/10965521/20c2c7d0e768/gr002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9a1/10965521/5e067fa245b6/gr003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9a1/10965521/4398572c7329/gr004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9a1/10965521/6db4827d3493/gr005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9a1/10965521/dcc16ff0b686/gr006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9a1/10965521/df37fd373333/gr007.jpg

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本文引用的文献

1
A general class of improved population variance estimators under non-sampling errors using calibrated weights in stratified sampling.一类在分层抽样中使用校准权重的非抽样误差下改进的总体方差估计量。
Sci Rep. 2024 Feb 5;14(1):2948. doi: 10.1038/s41598-023-47234-1.